Malaria continues to be a major health concern throughout much of the world - especially in Africa. The emergence of chloroquine-resistant strains of the malaria parasite and insecticide-resistant mosquitoes emphasizes the need for new and innovative methods of disease control. In this proposal, we are concerned with the hypothesis that transposable elements can be constructed that: (a) contain transgenes which will render the hosting mosquitoes (principally Anopholes gambiae) refractory to malaria, and (b) have the ability to be driven into the population through their ability to transpose replicatively throughout the genome. Similar proposals have been made for the control of dengue fever through genetic modification of its vector, Aedes aegypti. While disease control through genetic modification of vectors is intuitively appealing, there have been few mathematical analyses of the hypothesis. Support for the hypothesis presently comes from only a few computer simulations that often neglect key molecular biological details of the gene drive agents. Some sophisticated modeling has been directed at understanding the evolution and distribution of transposable elements in general. However, these analyses have almost exclusively focused on equilibrium distributions while it is the transient dynamics that are of interest to disease control. Our intent is to improve upon the models currently available and to incorporate additional levels of biological realism, as appropriate for the elements being considered for disease control. We will then apply these models to the questions that need answering of relevance to disease control. To achieve these goals, our specific aims will be: to analyze the temporal spread of the candidate transposable elements and imperfectly linked effector genes in An. gambiae host populations (Specific Aim #1), to incorporate site-specific effects such as preference for local transposition into the dynamics (Specific Aim #2), and to extend the implications of these models to larger geographical population structures affected by malaria (Specific Aim #3).